Ratings and Reviews for Introduction to Data Science in Python

Awesome content and engaging assignment ! Highly recommended to anyone interested in the application of python in data science.

It took a while to complete, but was worth it!

Fantastic topics and reading assignments. Like to see more challenging assignments.

Very effective course to start with Data Science in Pyhton.

A very nice introduction to libraries/skills used by data scientists. The auto-grader was extremely annoying though. Also, I felt that some of the questions on the assignments were a bit ambiguous.

Some assignments are ambiguous because the corrector is not updated... But i like it.

Really good problem sets and discussion forums. Unlike some other courses, this one doesn't provide much in the way of useful videos. Essentially, you get problem sets and have to search pandas documentation or StackOverflow for help, or look in the discussion forums. Still very useful in learning pandas, but the "classroom" part is not worthwhile.

Highly recommend taking this course to people with intermediate Python background. Especially, the assignments are great and very well prepared. Thank you!

I have mixed views about this course. The net result IS worthwhile and you definitely learn by being thrown in the deep end (this is not a softball "what's a for loop / Programming 101" type course.) First of all, be aware that the "estimated time of completion" for the assignments is low to put it very mildly: assignments that are estimated "90 minutes" may be more like eight to ten hours to complete (verified by many different course-takers, all of whom had extensive previous programming experience.) Do not take this course unless you can spend at least ten hours a week completing the assignments (unless you're already a prodigy in Python/Pandas -- but if so, why take this course?) Second of all, the lectures do not contain anywhere near all of the material you need to actually complete the assignments (the course creators even acknowledge this.) It took me a couple of assignments to realize this was so. It really made me think watching the lectures was a slight waste of time, so if you find yourself frustrated thinking you "missed something" because you don't know how to complete the assignment after viewing the lectures, you most likely _didn't_ miss anything: just expect to spend a lot of time Googling answers in order to find what you need. Third: the autograder here is really quirky. Once I got the hang of it and just reviewed the whole .py file generated to see where the problem was (versus just using the IPython window) it clicked pretty well, but I definitely spent a few hours flailing around trying to get my code to submit successfully. I hadn't had any issues with any other courses in this department. That being said: the skills learned are definitely "deep" and quantifiable and you get right into the thick of things after the first assignment. I'd venture to say that if you complete this entire 5 course sequence you'd probably have at least a passing knowledge of the subject matter for an interview in this field.

The course explores different ways to clear data using pandas. The assignments are challenging, since it is impossible to get to the solutions without going through lots of stackoverflow questions. The course also raises questions regarding the future of data and the problems behind the extensive use of pvalues, the p-hacking problem. Very interesting!